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      "source": [
        "---\n",
        "sidebar_label: In-memory\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ef1f0986",
      "metadata": {},
      "source": [
        "# MemoryVectorStore\n",
        "\n",
        "LangChain offers is an in-memory, ephemeral vectorstore that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. The default similarity metric is cosine similarity, but can be changed to any of the similarity metrics supported by [ml-distance](https://mljs.github.io/distance/modules/similarity.html).\n",
        "\n",
        "As it is intended for demos, it does not yet support ids or deletion.\n",
        "\n",
        "This guide provides a quick overview for getting started with in-memory [`vector stores`](/docs/concepts/#vectorstores). For detailed documentation of all `MemoryVectorStore` features and configurations head to the [API reference](https://api.js.langchain.com/classes/langchain.vectorstores_memory.MemoryVectorStore.html)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "70d54e56",
      "metadata": {},
      "source": [
        "## Overview\n",
        "\n",
        "### Integration details\n",
        "\n",
        "| Class | Package | PY support |  Package latest |\n",
        "| :--- | :--- | :---: | :---: |\n",
        "| [`MemoryVectorStore`](https://api.js.langchain.com/classes/langchain.vectorstores_memory.MemoryVectorStore.html) | [`langchain`](https://www.npmjs.com/package/langchain) | ❌ |  ![NPM - Version](https://img.shields.io/npm/v/langchain?style=flat-square&label=%20&) |"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "36fdc060",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "To use in-memory vector stores, you'll need to install the `langchain` package:\n",
        "\n",
        "This guide will also use [OpenAI embeddings](/docs/integrations/text_embedding/openai), which require you to install the `@langchain/openai` integration package. You can also use [other supported embeddings models](/docs/integrations/text_embedding) if you wish.\n",
        "\n",
        "```{=mdx}\n",
        "import IntegrationInstallTooltip from \"@mdx_components/integration_install_tooltip.mdx\";\n",
        "import Npm2Yarn from \"@theme/Npm2Yarn\";\n",
        "\n",
        "<IntegrationInstallTooltip></IntegrationInstallTooltip>\n",
        "\n",
        "<Npm2Yarn>\n",
        "  langchain @langchain/openai @langchain/core\n",
        "</Npm2Yarn>\n",
        "```\n",
        "\n",
        "### Credentials\n",
        "\n",
        "There are no required credentials to use in-memory vector stores.\n",
        "\n",
        "If you are using OpenAI embeddings for this guide, you'll need to set your OpenAI key as well:\n",
        "\n",
        "```typescript\n",
        "process.env.OPENAI_API_KEY = \"YOUR_API_KEY\";\n",
        "```\n",
        "\n",
        "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:\n",
        "\n",
        "```typescript\n",
        "// process.env.LANGSMITH_TRACING=\"true\"\n",
        "// process.env.LANGSMITH_API_KEY=\"your-api-key\"\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "93df377e",
      "metadata": {},
      "source": [
        "## Instantiation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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      "source": [
        "import { MemoryVectorStore } from \"langchain/vectorstores/memory\";\n",
        "import { OpenAIEmbeddings } from \"@langchain/openai\";\n",
        "\n",
        "const embeddings = new OpenAIEmbeddings({\n",
        "  model: \"text-embedding-3-small\",\n",
        "});\n",
        "\n",
        "const vectorStore = new MemoryVectorStore(embeddings);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ac6071d4",
      "metadata": {},
      "source": [
        "## Manage vector store\n",
        "\n",
        "### Add items to vector store"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "17f5efc0",
      "metadata": {},
      "outputs": [],
      "source": [
        "import type { Document } from \"@langchain/core/documents\";\n",
        "\n",
        "const document1: Document = {\n",
        "  pageContent: \"The powerhouse of the cell is the mitochondria\",\n",
        "  metadata: { source: \"https://example.com\" }\n",
        "};\n",
        "\n",
        "const document2: Document = {\n",
        "  pageContent: \"Buildings are made out of brick\",\n",
        "  metadata: { source: \"https://example.com\" }\n",
        "};\n",
        "\n",
        "const document3: Document = {\n",
        "  pageContent: \"Mitochondria are made out of lipids\",\n",
        "  metadata: { source: \"https://example.com\" }\n",
        "};\n",
        "\n",
        "const documents = [document1, document2, document3];\n",
        "\n",
        "await vectorStore.addDocuments(documents);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c3620501",
      "metadata": {},
      "source": [
        "## Query vector store\n",
        "\n",
        "Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent. \n",
        "\n",
        "### Query directly\n",
        "\n",
        "Performing a simple similarity search can be done as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "aa0a16fa",
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          "output_type": "stream",
          "text": [
            "* The powerhouse of the cell is the mitochondria [{\"source\":\"https://example.com\"}]\n",
            "* Mitochondria are made out of lipids [{\"source\":\"https://example.com\"}]\n"
          ]
        }
      ],
      "source": [
        "const filter = (doc) => doc.metadata.source === \"https://example.com\";\n",
        "\n",
        "const similaritySearchResults = await vectorStore.similaritySearch(\"biology\", 2, filter)\n",
        "\n",
        "for (const doc of similaritySearchResults) {\n",
        "  console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3ed9d733",
      "metadata": {},
      "source": [
        "The filter is optional, and must be a predicate function that takes a document as input, and returns `true` or `false` depending on whether the document should be returned.\n",
        "\n",
        "If you want to execute a similarity search and receive the corresponding scores you can run:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "5efd2eaa",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "* [SIM=0.165] The powerhouse of the cell is the mitochondria [{\"source\":\"https://example.com\"}]\n",
            "* [SIM=0.148] Mitochondria are made out of lipids [{\"source\":\"https://example.com\"}]\n"
          ]
        }
      ],
      "source": [
        "const similaritySearchWithScoreResults = await vectorStore.similaritySearchWithScore(\"biology\", 2, filter)\n",
        "\n",
        "for (const [doc, score] of similaritySearchWithScoreResults) {\n",
        "  console.log(`* [SIM=${score.toFixed(3)}] ${doc.pageContent} [${JSON.stringify(doc.metadata)}]`);\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c235cdc",
      "metadata": {},
      "source": [
        "### Query by turning into retriever\n",
        "\n",
        "You can also transform the vector store into a [retriever](/docs/concepts/retrievers) for easier usage in your chains:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "f3460093",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[\n",
            "  Document {\n",
            "    pageContent: 'The powerhouse of the cell is the mitochondria',\n",
            "    metadata: { source: 'https://example.com' },\n",
            "    id: undefined\n",
            "  },\n",
            "  Document {\n",
            "    pageContent: 'Mitochondria are made out of lipids',\n",
            "    metadata: { source: 'https://example.com' },\n",
            "    id: undefined\n",
            "  }\n",
            "]\n"
          ]
        }
      ],
      "source": [
        "const retriever = vectorStore.asRetriever({\n",
        "  // Optional filter\n",
        "  filter: filter,\n",
        "  k: 2,\n",
        "});\n",
        "\n",
        "await retriever.invoke(\"biology\");"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "423d779a",
      "metadata": {},
      "source": [
        "### Maximal marginal relevance\n",
        "\n",
        "This vector store also supports maximal marginal relevance (MMR), a technique that first fetches a larger number of results (given by `searchKwargs.fetchK`), with classic similarity search, then reranks for diversity and returns the top `k` results. This helps guard against redundant information:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "56817a1c",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[\n",
            "  Document {\n",
            "    pageContent: 'The powerhouse of the cell is the mitochondria',\n",
            "    metadata: { source: 'https://example.com' },\n",
            "    id: undefined\n",
            "  },\n",
            "  Document {\n",
            "    pageContent: 'Buildings are made out of brick',\n",
            "    metadata: { source: 'https://example.com' },\n",
            "    id: undefined\n",
            "  }\n",
            "]\n"
          ]
        }
      ],
      "source": [
        "const mmrRetriever = vectorStore.asRetriever({\n",
        "  searchType: \"mmr\",\n",
        "  searchKwargs: {\n",
        "    fetchK: 10,\n",
        "  },\n",
        "  // Optional filter\n",
        "  filter: filter,\n",
        "  k: 2,\n",
        "});\n",
        "\n",
        "await mmrRetriever.invoke(\"biology\");"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e2e0a211",
      "metadata": {},
      "source": [
        "### Usage for retrieval-augmented generation\n",
        "\n",
        "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
        "\n",
        "- [Tutorials: working with external knowledge](/docs/tutorials/#working-with-external-knowledge).\n",
        "- [How-to: Question and answer with RAG](/docs/how_to/#qa-with-rag)\n",
        "- [Retrieval conceptual docs](/docs/concepts/retrieval)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8a27244f",
      "metadata": {},
      "source": [
        "## API reference\n",
        "\n",
        "For detailed documentation of all `MemoryVectorStore` features and configurations head to the [API reference](https://api.js.langchain.com/classes/langchain.vectorstores_memory.MemoryVectorStore.html)."
      ]
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